Automated Single Cell Phenotyping of Time-of-Flight Secondary Ion Mass Spectrometry Tissue Images.

Existing analytical techniques are being improved or applied in new ways to profile the tissue microenvironment (TME) to better understand the role of cells in disease research. Fully understanding the complex interactions between cells of many different types and functions is often slowed by the intense data analysis required. Multiplexed Ion Beam Imaging (MIBI) has been developed to simultaneously characterize 50+ cell types and their functions within the TME with a subcellular spatial resolution, but this results in complex data sets that are challenging to qualitatively analyze.

Multiplexed ion beam imaging (MIBI) for characterization of the tumor microenvironment across tumor types.

An ability to characterize the cellular composition and spatial organization of the tumor microenvironment (TME) using multiplexed IHC has been limited by the techniques available. Here we show the applicability of multiplexed ion beam imaging (MIBI) for cell phenotype identification and analysis of spatial relationships across numerous tumor types. Formalin-fixed paraffin-embedded (FFPE) samples from tumor biopsies were simultaneously stained with a panel of 15 antibodies, each labeled with a specific metal isotope.

Fully convolutional neural network for removing background in noisy images of uranium bearing particles.

A fully convolutional neural network (FCN) was developed to supersede automatic or manual thresholding algorithms used for tabulating SIMS particle search data. The FCN was designed to perform a binary classification of pixels in each image belonging to a particle or not, thereby effectively removing background signal without manually or automatically determining an intensity threshold. Using 8000 images from 28 different particle screening analyses, the FCN was trained to accurately predict pixels belonging to a particle with near 99% accuracy.

Multimodal image fusion with SIMS: Preprocessing with image registration.

In order to utilize complementary imaging techniques to supply higher resolution data for fusion with secondary ion mass spectrometry (SIMS) chemical images, there are a number of aspects that, if not given proper consideration, could produce results which are easy to misinterpret. One of the most critical aspects is that the two input images must be of the same exact analysis area. With the desire to explore new higher resolution data sources that exists outside of the mass spectrometer, this requirement becomes even more important.

Cluster secondary ion mass spectrometry imaging of interfacial reactions of TiO2 microspheres embedded in ionic liquids.

Rationale: Our goal is to develop protocols for the elucidation of the identity and structure of reaction products embedded in a reaction medium. Results should find significance in a variety of disciplines ranging from the study of biological cells and tissues, to the steps associated with the functionalization of nanoparticles.

Methods: We utilize cluster secondary ion mass spectrometry (cluster-SIMS) to acquire three-dimensional (3D) information about 5-30 µm TiO2 microspheres imbedded into an ionic liquid.

Improving secondary ion mass spectrometry image quality with image fusion.

The spatial resolution of chemical images acquired with cluster secondary ion mass spectrometry (SIMS) is limited not only by the size of the probe utilized to create the images but also by detection sensitivity. As the probe size is reduced to below 1 μm, for example, a low signal in each pixel limits lateral resolution because of counting statistics considerations. Although it can be useful to implement numerical methods to mitigate this problem, here we investigate the use of image fusion to combine information from scanning electron microscope (SEM) data with chemically resolved SIMS images.